Results on recall. When it comes to the recall performances on the subsets, all of the previous methods enhance the overall accuracy at the expense of a large head performance drop.
For example, in Table REF , although LADE [1]} achieves a high top-1 accuracy of \(45.0\%\)  and few-short recall of \(29.3\%\) , its many-shot recall encounters with a significant decrease of \(8.4\%\)  compared to the baseline XE.
In contrast, our xERM achieves the highest overall performance with a competitive head performance against XE.
Surprisingly, xERM\(_{\!T\!D\!E}\)  is the first to beat XE w.r.t the recall performances on both CIFAR100LT-IB-100 and ImageNet-LT. These results demonstrate that our xERM achieves the unbiaseness from the factual and the counterfactual model, i.e., improving the overall accuracy without scarifying the head recall.
